A survey of current trends in computational drug repositioning

Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecu...

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Veröffentlicht in:	Briefings in bioinformatics Jg. 17; H. 1; S. 2 - 12
Hauptverfasser:	Li, Jiao, Zheng, Si, Chen, Bin, Butte, Atul J., Swamidass, S. Joshua, Lu, Zhiyong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Oxford Publishing Limited (England) 01.01.2016 Oxford University Press
Schlagworte:	Bioinformatics Computational Biology - trends Current Progress in Bioinformatics 2016 Papers Data Mining Data processing Drug Combinations Drug Repositioning - statistics & numerical data Drug Repositioning - trends drugs Experimental methods Genomics Genotype & phenotype Humans information processing Machine Learning Molecular Structure neoplasms Phenotype precision medicine Prescription drugs Repositioning surveys Surveys and Questionnaires genome phenome drug combination prediction validation computational drug repositioning chemical structure integrative strategies
ISSN:	1467-5463, 1477-4054, 1477-4054
Online-Zugang:	Volltext
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Abstract	Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.
AbstractList	Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning. Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential for precision medicine in the age of big data. The explosive growth of large-scale genomic and phenotypic data, as well as data of small molecular compounds with granted regulatory approval, is enabling new developments for computational repositioning. To achieve the shortest path toward new drug indications, advanced data processing and analysis strategies are critical for making sense of these heterogeneous molecular measurements. In this review, we show recent advancements in the critical areas of computational drug repositioning from multiple aspects. First, we summarize available data sources and the corresponding computational repositioning strategies. Second, we characterize the commonly used computational techniques. Third, we discuss validation strategies for repositioning studies, including both computational and experimental methods. Finally, we highlight potential opportunities and use-cases, including a few target areas such as cancers. We conclude with a brief discussion of the remaining challenges in computational drug repositioning.
Author	Zheng, Si Lu, Zhiyong Swamidass, S. Joshua Chen, Bin Li, Jiao Butte, Atul J.
Author_xml	– sequence: 1 givenname: Jiao surname: Li fullname: Li, Jiao – sequence: 2 givenname: Si surname: Zheng fullname: Zheng, Si – sequence: 3 givenname: Bin surname: Chen fullname: Chen, Bin – sequence: 4 givenname: Atul J. surname: Butte fullname: Butte, Atul J. – sequence: 5 givenname: S. Joshua surname: Swamidass fullname: Swamidass, S. Joshua – sequence: 6 givenname: Zhiyong surname: Lu fullname: Lu, Zhiyong
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/25832646$$D View this record in MEDLINE/PubMed
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Snippet	Computational drug repositioning or repurposing is a promising and efficient tool for discovering new uses from existing drugs and holds the great potential...
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SubjectTerms	Bioinformatics Computational Biology - trends Current Progress in Bioinformatics 2016 Papers Data Mining Data processing Drug Combinations Drug Repositioning - statistics & numerical data Drug Repositioning - trends drugs Experimental methods Genomics Genotype & phenotype Humans information processing Machine Learning Molecular Structure neoplasms Phenotype precision medicine Prescription drugs Repositioning surveys Surveys and Questionnaires
Title	A survey of current trends in computational drug repositioning
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